Electronic apparatus and method for summarizing content thereof

ABSTRACT

An example electronic apparatus and an example method for summarizing content thereof are provided. The example method includes displaying content on a display; based on receiving user input for content summarization, determining, as a content summarization range, from among content areas which are not displayed on the display, a content area corresponding to a location on the display at which the user input is detected; summarizing content within the content summarization range according to a type of the content; and displaying the summarized content along with the displayed content. The example electronic apparatus and example method may summarize the content by using a rule-based algorithm or an Artificial Intelligence (AI) algorithm.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/824,773, filed on Nov. 28, 2017, now U.S. Pat. No. 10,878,488, whichclaims priority to Korean Patent Application No. 10-2016-0160125, filedon Nov. 29, 2016, and to Korean Patent Application No. 10-2017-0144782,filed on Nov. 1, 2017. The contents of each of these applications areincorporated herein in their entirety.

BACKGROUND 1. Technical Field

The present disclosure generally relates to an electronic apparatus anda method for summarizing content, and more specifically, to anelectronic apparatus which summarizes content and provides summarizedcontent along with the content, and a corresponding method.

The present disclosure also generally relates to an ArtificialIntelligence (AI) system which simulates a cognitive or determiningfunction of a human brain by using a machine learning algorithm andapplications thereof.

2. Description of Related Art

With the recent increase of content, users are unable to check all ofthe content. Accordingly, it would be desirable to summarize content forthe user so that the user can check more content.

Recently, diverse methods for summarizing content have been developedfor a user to check a large amount of content more quickly andconveniently. However, according to conventional methods, there is aninconvenience in that entire content should be summarized, or that auser should designate a content summarization range one by one.

Further, according to conventional methods, the content is summarizedregardless of a type of the content, which decreases readability of thecontent.

Meanwhile, AI systems realizing human intelligence have been used invarious fields in recent years. Generally, an AI system is characterizedin that a machine learns, determines, and becomes smarter on its own,unlike existing rule-based smart systems. As a user uses an AI system,the AI system provides better recognition rate and better understandingof the user's taste or interests. In this regard, existing rule-basedsmart systems are being replaced with deep learning-based AI systems.

AI technologies include machine learning (for example, deep learning)and element technologies using the machine learning.

Machine learning refers to an algorithm technology in which a machineclassifies and learns characteristics of input data for itself. Theelement technologies refer to technologies of simulating cognitive ordetermining functions of a human brain by using a machine learningalgorithm, such as, deep learning, and may be divided into fields oflinguistic understanding, visual understanding, reasoning/prediction,knowledge representation, and operation control.

AI technologies may be applied to various fields. Linguisticunderstanding refers to technology for recognizing, applying, andprocessing verbal/written languages of a human and includes naturallanguage processing, machine translation, a conversation system,question and answer, and voice recognition/synthesis. Visualunderstanding refers to technology for recognizing and processingobjects in a human's viewpoint and includes object recognition, objecttracking, image search, human recognition, scene understanding, spaceunderstanding, and image improvement. Reasoning/prediction refers totechnology for determining information and executing logical reasoningand prediction and includes knowledge/probability-based reasoning,optimization prediction, preference-based planning, and recommendation.Knowledge representation refers to technology for processing humanexperience information to be automated knowledge data and includesknowledge construction (generating/classifying data) and knowledgemanagement (utilizing data). Operation control refers to technology forcontrolling automated driving of a vehicle and motion of a robot andincludes motion control (navigation, collision, driving) andmanipulation control (behavior control).

SUMMARY

An example aspect of the present disclosure provides an electronicapparatus which determines a content summarization range according to auser input and summarizes content within the content summarization rangebased on a type of the content and a method for summarizing contentthereof.

According to an example embodiment of the present disclosure, a methodfor summarizing content of an electronic apparatus includes displayingcontent, determining, based on user input for content summarizationbeing received, a content summarization range from content areas whichare not displayed on a display in accordance with a location at whichthe user input is detected, summarizing content within the contentsummarization range according to a type of the content, and displayingthe summarized content along with the content.

According to example another embodiment of the present disclosure, anelectronic apparatus includes a display, an input unit configured toreceive a user input, and a processor configured to display content onthe display, based on user input for content summarization beingreceived through the input unit, determine a content summarization rangefrom content areas which are not displayed on the display in accordancewith a location at which the user input is detected, summarize contentwithin the content summarization range according to a type of thecontent, and control the display to display the summarized content onthe content.

According to another example embodiment of the present disclosure, anon-transitory computer readable medium stores a program, that whenexecuted by a computer of an electronic apparatus, causes the computerto execute displaying content, determining, based on user input forcontent summarization being received, a content summarization range fromcontent areas which are not displayed on a display in accordance with alocation at which the user input is detected, summarizing content withinthe content summarization range according to a type of the content, anddisplaying the summarized content on the content.

According to the above-described various example embodiments of thepresent disclosure, the electronic apparatus may provide summarizedcontent by summarizing information corresponding to a user input frominformation which is not displayed in a display without summarizing theentire information of the content. Accordingly, a user is able to checkthe information which is not currently displayed more conveniently.

Further, the example electronic apparatus summarizes the content by adifferent summarization unit according to a type of the content and mayprovide the user with suitable summarized content.

BRIEF DESCRIPTION OF DRAWINGS

The above and/or other aspects, features and attendant advantages of thepresent disclosure will be more apparent and readily appreciated fromthe following detailed description, taken in conjunction with theaccompanying drawings, in which like reference numerals refer to likeelements, and wherein:

FIG. 1 is a block diagram illustrating a simple structure of anelectronic apparatus according to an example embodiment disclosedherein;

FIG. 2 is a block diagram illustrating a detailed structure of anelectronic apparatus according to an example embodiment disclosedherein;

FIGS. 3, 4A and 4B are block diagrams illustrating a structure of aprocessor according to an example embodiment disclosed herein;

FIGS. 5A, 5B and 5C are diagrams provided to describe an example ofselecting a content summarization range according to a location of auser input according to an example embodiment disclosed herein;

FIG. 6 is a diagram provided to describe a method for summarizingcontent according to an example embodiment disclosed herein;

FIGS. 7A, 7B, 8A. 8B, 9A, and 9B are diagrams provided to describe amethod for displaying summarized content according to various exampleembodiments disclosed herein;

FIG. 10A is a flowchart provided to describe a method for controlling anelectronic apparatus for content summarization according to an exampleembodiment disclosed herein;

FIG. 10B is a flowchart provided to describe an example in which a firstcomponent summarizes content by using a data summarization model;

FIG. 10C is a flowchart provided to describe an example in which asecond component and a third component summarize content respectively byusing a data summarization model according to a type of the content; and

FIG. 11 is a diagram provided to describe an example in which anelectronic apparatus summarizes data by using a data summarization modelin conjunction with a server according to another example embodimentdisclosed herein.

DETAILED DESCRIPTION

Certain example embodiments are described below in greater detail withreference to the accompanying drawings. In the following description,like drawing reference numerals are used for the like elements, even indifferent drawings. The matters defined in the present disclosure, suchas detailed construction and elements, are provided to assist in acomprehensive understanding of the example embodiments. However,embodiments can be practiced without those specifically defined matters.Also, well-known functions or constructions are not described in detailsince they would obscure the present disclosure with unnecessary detail.

In the following description, terms with an ordinal number, for example,‘first’ or ‘second,’ may be used to describe various elements, but theelements are not limited by the term. The terms including the ordinalnumber are used only to distinguish the same or similar elements. By wayof example, ‘first’ element may be referred to as ‘second’ element, andthe ‘second’ element may be also referred to as the ‘first’ elementwithout deviating from the scope of the present disclosure. The term‘and/or’ includes any one or combinations of a plurality of relatedelements.

The terms used in the following description are provided to describeexample embodiments and are not intended to limit the scope of thepresent disclosure. A term in a singular form includes a plural formunless it is intentionally written that way. In the followingdescription, a term, such as, ‘include’ or ‘have’, refers to thedisclosed features, numbers, steps, operations, elements, parts, orcombinations thereof and is not intended to exclude any possibilities ofexistence or addition of one or more other features, numbers, steps,operations, elements, parts, or combinations thereof.

A term ‘module’ or ‘unit’ refers to an element which performs one ormore functions or operations. The ‘module’ or ‘unit’ may be realized ashardware, software, or combinations thereof. A plurality of ‘modules’ or‘units’ may be integrated into at least one module and realized as atleast one processor, except for a case in which the respective ‘modules’or ‘units’ need to be realized as discrete specific hardware.

Hereinafter, the example embodiments of the present disclosure will bedescribed in detail with reference to the accompanying drawings. FIG. 1is a block diagram illustrating a simple structure of an electronicapparatus 100 according to an example embodiment disclosed herein. Asillustrated in FIG. 1, the electronic apparatus 100 includes a display110, an input unit 120, and a processor 130. In this case, theelectronic apparatus 100 may be a smart phone, but this is only anexample. The electronic apparatus 100 may be realized as diverse kindsof electronic apparatuses, such as, without limitation, a tabletPersonal Computer (PC), a notebook PC, a Personal Digital Assistant(PDA), a smart TV, a projection TV, a kiosk, and so on.

The display 110 outputs image data. To be specific, the display 110 maydisplay image content. In this case, the image content may include,without limitation, at least one of text, an image, video, and audio andmay be one of various kinds of content, such as, news content, e-bookcontent, or shopping content.

The input unit (e.g., input devices and associated input circuitry) 120receives a user input for controlling the electronic apparatus 100. Tobe specific, the input unit 120 may receive a user input for summarizingcontent displayed in the display 110. In this case, the user input maybe a user touch of touching a certain area of the display 110.

The processor (e.g., processing circuitry) 130 controls overalloperations of the electronic apparatus 100. To be specific, based onuser input for content summarization being received through the inputunit 120, the processor 130 may determine a content summarization rangefrom content areas which are not displayed on the display according to alocation where the user input was detected, summarize the content withinthe content summarization range according to a type of the content, andcontrol the display 110 to display the summarized content on thecontent.

To be specific, based on receiving user input of touching any of upper,lower, right, and left areas of the display, the processor 130 maydetermine, as a content summarization range, an area corresponding to alocation at which the user input was input from among the content areaswhich are not displayed on the display. For example, in response toreceiving a user input of touching an upper area of the display, theprocessor 130 may determine the upper area which is not displayed in thedisplay as the content summarization range.

The processor 130 may determine a type of the content by analyzing thecontent and summarize the content by a different content summarizationunit according to the type of the content. In this case, the processor130 may determine the type of the content by analyzing the text, image,and video included in the content. The content summarization unit may bea unit for summarizing the content, and a paragraph, a chapter, a page,or an entire document may be the content summarization unit.

The processor 130 may determine the content summarization unit accordingto the type of the user input and summarize the content within thecontent summarization range by the determined content summarizationunit. By way of example, based on a user input being a first touch(e.g., a touch within or less than a predetermined time), the processor130 may summarize the content by a first content summarization unit (forexample, a paragraph) and, in response to the user input being a secondtouch (e.g., a touch beyond or greater than a predetermined time), theprocessor 130 may summarize the content by a second contentsummarization unit (for example, an entire document).

To be specific, the processor 130 may analyze information of the contentwithin the content summarization range, extract main text for a summarysentence based on the analyzed information, and generate summarizedcontent by editing and arranging the extracted text. In this case, theprocessor 130 may summarize the content within the content summarizationrange based on a pre-stored data summarization model. This operationwill be described below in further detail with reference to FIGS. 3. 4A,and 4B.

Based on receiving a user input for scrolling a screen while thesummarized content is displayed, the processor 130 may control thedisplay 110 to display summarized content having a content summarizationrange changed according to screen scrolling corresponding to the userinput while a screen of the content is scrolled.

Further, based on user input being received for the summarized content,the processor 130 may control the display 110 to display an areacorresponding to a part where the user input was received for thesummarized content.

FIG. 2 is a block diagram illustrating a detailed structure of anelectronic apparatus 100 according to an example embodiment disclosedherein. As illustrated in FIG. 2, the electronic apparatus 100 includesa display 110, an input unit 120, a memory 140, a communicator 150, animage processor 160, an audio processor 170, an audio output unit 180,and a processor 130.

The display 110 may display diverse image content, information, or aUser Interface (UI) provided by the electronic apparatus 100. To bespecific, the display 110 may display one of diverse image content (forexample, news content, e-book content, blog content, or shoppingcontent).

The display 110 may display the summarized content generated by the userinput along with the image content.

The input unit 120 may receive user input for controlling the electronicapparatus 100 and transmit information based on the user input to theprocessor 130. For example, the input unit 120 may include, withoutlimitation, a touch panel, a (digital) pen sensor, or a key. By way ofexample, the touch panel may be realized as at least one of a capacitivetype, a pressure-resistive type, an infrared-ray type, or an ultrasonicwave type. The touch panel may further include a control circuit. Thetouch panel may further include a tactile layer and provide a user witha tactile response. The (digital) pen sensor may be realized as a partof the touch panel or include a summarization sheet, for example. Thekey may include a physical button, an optical key, or a keypad, forexample.

In addition, the input unit 120 may be realized as various kinds ofinput devices, such as, without limitation, a keyboard, a mouse, apointing device, a user motion summarization device, or the like.

The memory 140 may store diverse programs and data necessary foroperations of the electronic apparatus 100. The memory 140 may berealized as, for example, a non-volatile memory, a volatile memory, aflash memory, a Hard Disk Drive (HDD), or a Solid State Drive (SSD). Thememory 140 may be accessed by the processor 130, and the data in thememory may be read, recorded, modified, deleted, or updated by theprocessor 130. In the present disclosure, the term ‘memory’ may includethe memory 140, a Read-Only Memory (ROM, not shown) in the processor, aRandom Access Memory (RAM, not shown), or a memory card (not shown)installed in the electronic apparatus 100 (for example, a micro SecureDigital (SD) card or a memory).

The memory 140 may store programs and data for various screens to bedisplayed in a display area of the display 110. Further, the memory 140may store a data summarization model generated by a learning algorithmfor content summarization according to an example embodiment disclosedherein.

The communicator (e.g., communication circuitry) 150 may communicatewith various types of external apparatuses according to diversecommunication methods. The communicator 150 may include, withoutlimitation, at least one of a Wireless-Fidelity (Wi-Fi) chip, aBluetooth chip, a wireless communication chip, and a Near FieldCommunication (NFC) chip. The processor 130 may communicate with anexternal server or a various external apparatuses by controlling thecommunicator 150.

To be specific, the communicator 150 may receive the data summarizationmodel for generating content from the external server.

The image processor (e.g., image processing circuitry such as a graphicsprocessing unit (GPU) 160 may perform image processing with respect tothe image data received from various sources. To be specific, the imageprocessor 160 may perform various image processing operations, such as,decoding, scaling, noise filtering, frame rate conversion, andresolution conversion, with respect to the image data.

The audio processor (e.g., audio processing circuitry) 170 may processaudio data. For example, the audio processor 170 may perform decoding,amplification, or noise filtering with preset to the audio data.

The audio output unit (e.g., speakers and associated circuitry) 180 mayoutput various audio data processed by the audio processor 170 and alsooutput various notification sounds or voice messages.

The processor 130 may be electrically connected with the components ofthe electronic apparatus 100 (for example, the display 110, the memory140, and the input unit 120) and control overall operations andfunctions of the electronic apparatus 100. Particularly, the processor130 may control the overall operations of the electronic apparatus 100by using the diverse programs stored in the memory 140.

To be specific, in response to a user input for content summarizationbeing received through the input unit 120 while the content is displayedin the display 110, the processor 130 may determine a contentsummarization range according to a location where the user input wasdetected, summarize the content within the content summarization rangeaccording to the type of the content, and control the display 110 todisplay the summarized content, for example, on the content. Meanwhile,according to an example embodiment disclosed herein, the processor 130may summarize the content by using a data summarization model generatedby using a learning algorithm. This operation will be described below infurther detail with reference to FIGS. 3, 4A, and 4B.

In some example embodiments, the processor 130 may include a datalearning unit 131 and a data summarizing unit 132 as illustrated in FIG.3.

The data learning unit 131 may learn criteria for content summarization.To be specific, the data learning unit 131 may learn the criteria as towhich model to use in order to determine the content summarization or asto how to generate summarized content by using the summarized data forlearning. The data learning unit 131 may learn the criteria for contentsummarization by acquiring the summarized data for learning to be usedin the learning operation and applying the acquired summarized data forlearning to the data summarization model. A detailed description on thedata summarization model will be provided below.

The data learning unit 131 may allow the data summarization model tolearn by using the content and generate the data summarization model forsummarizing the content. In this case, the content may include at leastone of text, image, and video.

The data learning unit 131 may allow the data summarization model tolearn by using the content and the summarized content of the content aslearning data.

According to an example embodiment, the data summarization model may bea model configured to summarize content. In this case, the learning datamay be text and summarized text of the text.

As an example, the learning data may be the following text and thesummarized text thereof: “the Meteorological Administration expects thattoday's weather will be affected by a pressure trough which will stay onthe border of an anticyclone located in the central part of China andthen go through the south. Accordingly, it is expected that the weatherof the whole country will be mostly cloudy, and most parts of thecountry except for Seoul and Gyeonggi-do will experience occasionalshowers from noon and evening. Some rain is expected during the day inSeoul, Gyeonggi-do, and the northwestern part of Gangwon-do, and acloudy sky and occasional showers caused by an east wind are expected inthe eastern part of Gangwon-do and the eastern coast of Gyeongsang-do.The expected rainfall in the eastern part of Gangwondo, the south coastof Jeollanam-do, Gyeongsang-do, Ulleung-do, and Dok-do is about 5 to 40mm, and the expected rainfall in the southwestern part of Gangwon-do,Chungcheong-do, Jeolla-do, and Cheju-do is about 5 mm. The morning's lowtemperature will range from 7 to 17 degrees, and the daytime's hightemperature will range from 13 to 21 degrees. The temperature will dropa lot from the daytime of today to the 13th of this month due to coldair from the northwest.” According to an embodiment, the summarized textmay be generated by extracting and editing nouns, verbs, and adjectiveswhich are frequently shown in the text. By way of example, thesummarized text may be ‘Seoul, Gyeonggi-do, Gangwon-do, raining ineastern coast of Gyeongsang-do, temperature drops’ by using thefrequently shown words ‘Seoul,’ ‘Gyeonggi-do,’ ‘Gangwon-do,’ ‘easterncoast of Gyeongsang-do,’ ‘rain,’ and ‘temperature.’

According to various example embodiments, the data summarization modelmay be a model configured to summarize a plurality of images. In thiscase, the learning data may be the plurality of images and arepresentative image thereof.

As an example, the learning data may be a plurality of images displayedin a web page of a shopping mall and the largest image or an imagedisplayed in the uppermost part of the web page.

According to various example embodiments, the data summarization modelmay be a model configured to summarize video. In this case, the learningdata may be the video and a summarized video or a plurality of images ofthe video.

As an example, the learning data may be a plurality of images or a videoobtained by extracting and combining a frame in which a person appearsor a frame in which a location is changed from the video and the framesof the video.

The above-described data summarization models for summarizing text, aplurality of images, and video may be the same recognition model or maybe different models. The respective data summarization models mayinclude a plurality of data recognition models.

The data summarizing unit 132 may perform content summarization based onthe summarized data. The data summarizing unit 132 may summarize contentfrom certain data by using the learned data summarization model. Thedata summarizing unit 132 may acquire certain summarized data forrecognition based on predetermined criteria by learning and use the datasummarization model by using the acquired summarized data forrecognition as an input value in order to perform the contentsummarization based on the certain summarized data for recognition. Aresult value outputted by the data summarization model using theacquired summarized data for recognition as the input value may be usedto update the data summarization model.

The data summarizing unit 132 may estimate the summarized content byapplying the content to the data summarization model. For example, thedata summarizing unit 132 may acquire recognition data by applying thecontent to the data summarization model and providing the acquiredrecognition data to a processor of the electronic apparatus 100 (forexample, the processor 130 of FIG. 1). The processor 130 may summarizethe acquired recognition data.

According to an example embodiment, the data summarization model may bea model configured to summarize text. In this case, the data summarizingunit 132 may estimate summarized information of the text by applying thetext to the data summarization model as recognition data. For example,the data summarization model may summarize the text by using nouns,verbs, and adjectives which are frequently shown in the text andsentences including the same.

According to various example embodiments, the data summarization modelmay be a model configured to summarize a plurality of images. In thiscase, the data summarizing unit 132 may estimate a representative imageby applying the plurality of images to the data summarization model asrecognition data.

According to various example embodiments, the data summarization modelmay be a model configured to summarize video. In this case, the datasummarizing unit 132 may estimate summarized information of the video byapplying the video to the data summarization model as recognition data.For example, the data summarization model may acquire a summarized videoor a plurality of images by using a frame in which a persona appears ina large size or a frame in which a location is changed from among theframes of the video.

At least one of the data learning unit 131 and the data summarizing unit132 may be realized as at least one hardware chip and installed in theelectronic apparatus 100. By way of example, at least one of the datalearning unit 131 and the data summarizing unit 132 may be realized as adedicated hardware chip for AI or may be realized as a part of anexisting universal general-purpose processor (for example, a CentralProcessing Unit (CPU) or an application processor) or a part of aGraphic Processing Unit (GPU) and installed in the above-describedvarious electronic apparatuses.

According to an example embodiment, the dedicated hardware chip for AImay be a dedicated processor specialized for probability calculation andprovide higher parallel processing performance as compared with theexisting universal general-purpose processor. Accordingly, the dedicatedhardware chip for AI may quickly execute a calculation operation for theAI field, such as, machine learning.

In this case, the data learning unit 131 and the data summarizing unit132 may be installed in one electronic apparatus 100 or may be installedin different electronic apparatuses, respectively. For example, one ofthe data learning unit 131 and the data summarizing unit 132 may beincluded in the electronic apparatus, and the other one may be includedin a server. Further, the data learning unit 131 and the datasummarizing unit 132 may be connected in a wired and/or wireless mannerand transmit model information built by the data learning unit 131 tothe data summarizing unit 132 or transmit data inputted in the datasummarizing unit 132 to the data learning unit 131 as additionallearning data.

At least one of the data learning unit 131 and the data summarizing unit132 may be realized as a software module. In response to at least one ofthe data learning unit 131 and the data summarizing unit 132 beingrealized as a software module (or a program module includinginstructions), the software module may be stored in a non-transitorycomputer readable medium. In this case, at least one software module maybe provided by an Operating System (OS) or by a certain application.Further, some of the at least one software module may be provided by theOS, and the other may be provided by the certain application.

FIG. 4A is a block diagram illustrating the data learning unit 131according to some example embodiments disclosed herein.

Referring to FIG. 4A, the data learning unit 131 according to someexample embodiments may include a data acquisition part 131-1, apreprocessing part 131-2, a learning data selection part 131-3, a modellearning part 131-4, and a model evaluation part 131-5. According to anexample embodiment, the data learning unit 131 may include the dataacquisition part 131-1 and the model learning part 131-4 as essentialcomponents and selectively include at least one or none of thepreprocessing part 131-2, the learning data selection part 131-3, andthe model evaluation part 131-5.

The data acquisition part 131-1 may acquire summarized data for learningwhich is necessary for the content summarization. The data acquisitionpart 131-1 may acquire the summarized data for learning which isnecessary for the learning operation for the content summarization. Inthis case, the data acquisition part 131-1 may acquire image contentreceived from an external apparatus or a server. The acquired imagecontent may include at least one of text and an image and may be dividedby a paragraph, a chapter, and a page.

The preprocessing part 131-2 may preprocess the acquired summarized datafor learning so as to be used in the learning operation for the contentsummarization. The preprocessing part 131-2 may process the acquireddata to be in a predetermined format so the model learning part 131-4uses the acquired data for the learning operation for the contentsummarization. A detailed description on the model learning part 131-4will be provided below.

As an example, in response to the summarized data for learning beingtext data, the preprocessing part 131-2 may perform a preprocessingoperation, such as, sentence segmentation, part-of-speech tagging,tokenization, elimination of stop words, or stem extraction, withrespect to the text data. As another example, in response to thesummarized data for learning being image data, the preprocessing part131-2 may perform processing operations, such as, decoding, scaling,noise filtering, or resolution conversion, with respect to the imagedata in order to make image frames in the same format. Further, thepreprocessing part 131-2 may crop only a particular area from each of aplurality of inputted image frames.

The learning data selection part 131-3 may select the summarized datafor learning which is necessary for the learning operation from thepreprocessed summarized data for learning. The selected summarized datafor learning may be provided to the model learning part 131-4. Thelearning data selection part 131-3 may select the summarized data forlearning which is necessary for the learning operation from thepreprocessed summarized data for learning according to predeterminedcriteria for the content summarization. Further, the learning dataselection part 131-3 may select the data according to the criteriapredetermined by the learning operation by the model learning part131-4. A detailed description on the model learning part 131-4 will beprovided below.

In this case, the learning data selection part 131-3 may select thesummarized data for learning according to the type of the inputtedcontent. For example, in response to the content being news content, thelearning data selection part 131-3 may select only the data on areasexcluding advertisement data included in the news content. The learningdata selection part 131-3 may select some of the summarized data forlearning from the preprocessed summarized data for learning, but this isonly an example, and the learning data selection part 131-3 may selectall of the preprocessed summarized data for learning.

The learning data selection part 131-3 may select the summarized datafor learning according to a user input. For example, in response to auser input being a first touch, the learning data selection part 131-3may select a paragraph as the summarized data for learning, and inresponse to the user input being a second touch, the learning dataselection part 131-3 may select a passage as the summarized data forlearning.

According to an example embodiment, the learning data selection part131-3 may select the learning data before the preprocessing operation ofthe preprocessing part 131-2, needless to say.

The model learning part 131-4 may learn the criteria as to how tosummarize the content based on the learning data. Further, the modellearning part 131-4 may learn the criteria as to which learning data touse for the content summarization.

The model learning part 131-4 may allow the data summarization modelused for the content summarization to learn by using the learning data.In this case, the data summarization model may be a prebuilt model. Byway of example, the data summarization model may be a model which wasprebuilt by receiving basic learning data (for example, text data orimage data).

The data summarization model may be built by considering applicationareas of a summarization model, a purpose of learning, or computerperformance of an apparatus. The data summarization model may be a modelbased on a neural network, for example. The data summarization model maybe configured so as to simulate a structure of a human brain on acomputer. The data summarization model may include a plurality ofnetwork nodes which have weighted values and simulate neurons of a humanneuropilus. The plurality of network nodes may be interconnectedrespectively so as to simulate synaptic activity of the neurons whichexchange signals through a synapse. The data summarization model mayinclude a neural network model or a deep-learning model developed fromthe neural network model, for example. In the deep-learning model, theplurality of network nodes may be located in different depths (orlayers) and exchange data according to a convolution connection. By wayof example, models, such as a Deep Neural Network (DNN), a RecurrentNeural Network (RNN), or a Bidirectional Recurrent Deep Neural Network(BRDNN), may be used as the data summarization model, but the presentdisclosure is not limited in this respect.

According to various example embodiments, based on a plurality ofprebuilt data summarization models being present, the model learningpart 131-4 may determine a data summarization model having highrelevancy between the inputted learning data and the basic learning dataas a data summarization model to learn. In this case, the basic learningdata may be pre-classified according to a type of the data, and the datasummarization model may be prebuilt according to the type of the data.As an example, the basic learning data may be pre-classified accordingto various criteria such as, without limitation, a generated area, agenerated time, a size, a genre, a constructor, and a type of objects ofthe learning data.

Further, for example, the model learning part 131-4 may allow the datasummarization model to learn by using a learning algorithm including anerror back-propagation method or a gradient descent method.

The model learning part 131-4 may allow the data summarization model tolearn through supervised learning using the learning data as an inputvalue, for example. The model learning part 131-4 may also allow thedata summarization model to learn through unsupervised learning whichallows the data summarization model to learn a type of data necessaryfor the content summarization for itself without supervision, forexample. Further, the model learning part 131-4 may allow the datasummarization model to learn through reinforcement learning usingfeedback as to whether a result of the content summarization accordingto the learning is correct.

Further, in response to the data summarization model learned, the modellearning part 131-4 may store the learned data summarization model. Inthis case, the model learning part 131-4 may store the learned datasummarization model in the memory 140 of the electronic apparatus 100which includes the data summarizing unit 132. Further, the modellearning part 131-4 may store the learned data summarization model in amemory of a server which is connected with the electronic apparatus 100through a wired and/or wireless network.

In this case, the memory 140 may store instructions or data related toat least one other component of the electronic apparatus together withthe learned data summarization model, for example. Further, the memory140 may store software and/or a program. The program may include kernel,middleware, an Application Programming Interface (API), and/or anapplication program (or ‘application’), for example.

The model evaluation part 131-5 may input evaluation data in the datasummarization model, and, in response to a summarization resultoutputted from the evaluation data not satisfying a predeterminedcriterion, allow the model learning part 131-4 to learn again. In thiscase, the evaluation data may be predetermined data for evaluating thedata summarization model.

By way of example, in response to the number or a ratio of theevaluation data in which the summarization result is incorrect among thesummarization results of the learned data summarization model withrespect to the evaluation data exceeding a predetermined thresholdvalue, the model evaluation part 131-5 may evaluate that thesummarization result does not satisfy the predetermined criterion. Forexample, assume that there are 1,000 evaluation data, and thepredetermined criterion is defined as 2%. In this case, in response tothe learned data summarization model outputting incorrect summarizationresults with respect to more than 20 evaluation data, the modelevaluation part 131-5 may evaluate that the learned data summarizationmodel is not suitable.

In response to a plurality of learned data summarization models beingpresent, the model evaluation part 131-5 may evaluate whether therespective learned data summarization models satisfy the predeterminedcriterion and decide a model satisfying the predetermined criterion as afinal data summarization model. In this case, in response to a pluralityof models satisfying the predetermined criterion, the model evaluationpart 131-5 may decide any predetermined one or a certain number ofmodels as the final data summarization model in the order of highevaluation scores.

At least one of the data acquisition part 131-1, the preprocessing part131-2, the learning data selection part 131-3, the model learning part131-4, and the model evaluation part 131-5 in the data learning unit 131may be realized as at least one hardware chip and installed in theelectronic apparatus. By way of example, at least one of the dataacquisition part 131-1, the preprocessing part 131-2, the learning dataselection part 131-3, the model learning part 131-4, and the modelevaluation part 131-5 may be realized as a dedicated hardware chip forAI or may be realized as a part of the existing universalgeneral-purpose processor (for example, a CPU or an applicationprocessor) or a part of a GPU and installed in the above-describedvarious electronic apparatuses.

Further, the data acquisition part 131-1, the preprocessing part 131-2,the learning data selection part 131-3, the model learning part 131-4,and the model evaluation part 131-5 may be installed in the electronicapparatus 100 or installed in different electronic apparatuses,respectively. For example, some of the data acquisition part 131-1, thepreprocessing part 131-2, the learning data selection part 131-3, themodel learning part 131-4, and the model evaluation part 131-5 may beincluded in the electronic apparatus, and others may be included in aserver, for example.

At least one of the data acquisition part 131-1, the preprocessing part131-2, the learning data selection part 131-3, the model learning part131-4, and the model evaluation part 131-5 may be realized as a softwaremodule. In response to at least one of the data acquisition part 131-1,the preprocessing part 131-2, the learning data selection part 131-3,the model learning part 131-4, and the model evaluation part 131-5 beingrealized as a software module (or a program module includinginstructions), the software module may be stored in a non-transitorycomputer readable medium. In this case, at least one software module maybe provided by the OS or by a certain application. Further, some of theat least one software module may be provided by the OS, and the othermay be provided by the certain application.

FIG. 4B is a block diagram illustrating the data summarizing unit 132according to some example embodiments disclosed herein.

Referring to FIG. 4B, the data summarizing unit 132 according to someexample embodiments may include a data acquisition part 132-1, apreprocessing part 132-2, a summarized data selection part 132-3, asummarization result providing part 132-4, and a model update part132-5. According to an example embodiment, the data summarizing unit 132may include the data acquisition part 132-1 and the summarization resultproviding part 132-4 as essential components and selectively include atleast one or none of the preprocessing part 132-2, the summarized dataselection part 132-3, and the model update part 132-5.

The data acquisition part 132-1 may acquire the data necessary for thecontent summarization. For example, the data acquisition part 132-1 mayacquire at least one of text, image, and video.

The preprocessing part 132-2 may preprocess the acquired summarized datafor recognition so as to be used for content summarization. Thepreprocessing part 132-2 may process the acquired summarized data forrecognition to be in a predetermined format so the summarization resultproviding part 132-4 uses the acquired summarized data for recognitionfor the content summarization. A detailed description on thesummarization result providing part 132-4 will be provided below.

The summarized data selection part 132-3 may select the summarized datafor recognition which is necessary or used for content summarizationfrom the preprocessed summarized data for recognition. The selectedsummarized data for recognition may be provided to the summarizationresult providing part 132-4. The summarized data selection part 132-3may select some or all of the preprocessed summarized data forrecognition according to the predetermined criteria for the contentsummarization. Further, the summarized data selection part 132-3 mayselect the summarized data for recognition according to the criteriapredetermined by the learning of the model learning part 131-4. Adetailed description on the model learning part 131-4 will be providedbelow.

The summarization result providing part 132-4 may summarize the contentby applying the selected summarized data for recognition to the datasummarization model. The summarization result providing part 132-4 mayprovide a summarization result according to a summarization purpose ofdata. The summarization result providing part 132-4 may apply theselected data to the data summarization model by using the summarizeddata for recognition selected by the summarized data selection part132-3 as an input value. The summarization result may be determined bythe data summarization model.

For example, the summarization result of news content and e-book contentmay be provided in the form of text, and the summarization result ofshopping content may be provided in forms of text and an image(s).

The model update part 132-5 may update the data summarization modelbased on evaluation with respect to the summarization result provided bythe summarization result providing part 132-4. For example, the modelupdate part 132-5 may provide the summarization result received from thesummarization result providing part 132-4 to the model learning part131-4 so the model learning part 131-4 updates the data summarizationmodel.

At least one of the data acquisition part 132-1, the preprocessing part132-2, the summarized data selection part 132-3, the summarizationresult providing part 132-4, and the model update part 132-5 in the datasummarizing unit 132 may be realized as at least one hardware chip andinstalled in the electronic apparatus 100. By way of example, at leastone of the data acquisition part 132-1, the preprocessing part 132-2,the summarized data selection part 132-3, the summarization resultproviding part 132-4, and the model update part 132-5 may be realized asa dedicated hardware chip for AI or may be realized as a part of theexisting universal general-purpose processor (for example, a CPU or anapplication processor) or a part of a GPU and installed in theabove-described various electronic apparatuses.

The data acquisition part 132-1, the preprocessing part 132-2, thesummarized data selection part 132-3, the summarization result providingpart 132-4, and the model update part 132-5 may be installed in theelectronic apparatus 100 or installed in different electronicapparatuses, respectively. For example, some of the data acquisitionpart 132-1, the preprocessing part 132-2, the summarized data selectionpart 132-3, the summarization result providing part 132-4, and the modelupdate part 132-5 may be included in the electronic apparatus, and theother may be included in a server or other computer.

At least one of the data acquisition part 132-1, the preprocessing part132-2, the summarized data selection part 132-3, the summarizationresult providing part 132-4, and the model update part 132-5 may berealized as a software module. Based on at least one of the dataacquisition part 132-1, the preprocessing part 132-2, the summarizeddata selection part 132-3, the summarization result providing part132-4, and the model update part 132-5 being realized as a softwaremodule (or a program module including instructions), the software modulemay be stored in a non-transitory computer readable medium. In thiscase, at least one software module may be provided by the OS or by acertain application. Further, some of the at least one software modulemay be provided by the OS, and the other may be provided by the certainapplication.

As described above, the processor 130 may summarize the content by usingthe data summarization model generated by using the learning algorithm.

Hereinafter, a method for summarizing content according to an embodimentwill be described with reference to FIGS. 5A, 5B, 5C, 6, 7A, 7B, 8A, 8B,9A, and 9B.

The processor 130 may control the display 110 to display content. Inthis case, the content may be image content including at least one oftext, an image, and a video. By way of example, the content may includenews content, e-book content, or web page content (for example, blogcontent or shopping content).

In this case, the processor 130 may control the display 110 to display apart of the content without displaying the entire content.

The processor 130 may enter a content summarization mode according to auser input. The content summarization mode may refer to a mode forsummarizing content according to a user input of touching a certain areaof the display. In response to a mode of the electronic apparatus 100being converted to the content summarization mode, the processor 130 maycontrol the display 110 or the audio output unit 180 to output anindicator which shows or indicates that the current mode is the contentsummarization mode. The content summarization mode is only an example,and the processor 130 may perform content summarization through a userinput without entering the content summarization mode.

The processor 130 may receive a user input for content summarizationthrough the input unit 120 while the content is displayed. In this case,the user input may be a user touch of touching a certain area of thedisplay 110. In this case, the user touch may include various kinds ofuser touches, such as, a tap-touch input of tapping the display 110, atouch-and-hold input of maintaining a touch for a predetermined time,and a double-touch input of tapping the display 100 twice within apredetermined period of time.

The processor 130 may determine the content summarization rangeaccording to a location at which the user input was detected. To bespecific, when user input of touching any of the upper, lower, right,and left areas of the display is received, the processor 130 may checkan area which is currently displayed in the display 110 and determine anarea corresponding to a location at which the user input was detectedamong the content areas which are not displayed in the display as thecontent summarization range.

As an example, as illustrated in FIG. 5A, in response to receiving auser input 505 of touching an upper area while a part of the content isdisplayed, the processor 130 may determine an area 510 which is locatedin an upper part among the content areas which are not displayed in thedisplay as the content summarization range. As another example, asillustrated in FIG. 5B, in response to receiving a user input 515 oftouching a lower area while a part of the content is displayed, theprocessor 130 may determine an area 520 which is located in a lower partamong the content areas which are not displayed in the display as thecontent summarization range. As still another example, as illustrated inFIG. 5C, in response to receiving a user input 525 of touching a rightpart while a part of the content including pages is displayed, theprocessor 130 may determine pages 530 which are located on the rightside among the content areas which are not displayed in the display asthe content summarization range.

Further, the processor 130 may determine a size of the contentsummarization range according to the type of the user input. Forexample, in response to the tap-touch input with respect to the upperpart being detected while a part of the content is displayed, theprocessor 130 may determine a paragraph located in an upper part of acurrently displayed screen among the content areas which are notdisplayed in the display as the content summarization range. Further, inresponse to the touch-and-hold input with respect to the upper partbeing detected while a part of the content is displayed, the processor130 may determine entire areas located in the upper part of thecurrently displayed screen among the content areas which are notdisplayed in the display as the content summarization range.

The processor 130 may summarize the content within the contentsummarization range according to the type of the content. The processor130 may determine a type of the content by analyzing the currentcontent. To be specific, the processor 130 may determine the type of thecurrently displayed content by analyzing the text or image included inthe current content.

The processor 130 may summarize the content by different contentsummarization units based on the type of content. Content summarizationunit may refer to a unit for summarizing the content, and the contentsummarization may be performed for each content summarization unit. Inthis case, a paragraph, a chapter, a page, and an entire document may,for example, be the content summarization unit.

According to an example embodiment, based on the type of currentlydisplayed content being news content or blog content, the processor 130may determine a paragraph as the content summarization unit. In thiscase, the processor 130 may analyze tag information on a source code ofa web page and recognize the paragraph which is the contentsummarization unit. Based on the type of currently displayed contentbeing magazine content or e-book content, the processor 130 maydetermine a section as the content summarization unit. In this case, theprocessor 130 may recognize a section unit by including a subtitle or asentence including a bold font. Based on the type of currently displayedcontent being application content including a plurality of pages, theprocessor 130 may determine a page as the content summarization unit.

Further, the processor 130 may summarize the content by differentcontent summarization units according to user input. For example, inresponse to the tap-touch input with respect to the upper part beingdetected while a part of the content is displayed, the processor 130 maydetermine a paragraph as the content summarization unit. Further, inresponse to the touch-and-hold input with respect to the upper partbeing detected while a part of the content is displayed, the processor130 may determine an entire upper part as the content summarizationunit.

The processor 130 may perform content summarization for each contentsummarization unit. For example, based on three paragraphs being presentin the content summarization range, and the content summarization unitbeing defined as a paragraph, the processor 130 may perform the contentsummarization for each of the three paragraphs. Further, based on threeparagraphs being present in the content summarization range, and thecontent summarization unit being defined as an entire area, theprocessor 130 may perform the content summarization for all threeparagraphs.

Particularly, text summarization may be executed through threeoperations of analysis 610, transformation 620, and synthesis 630 asillustrated in FIG. 6. In the analysis operation 610, information on thecontent within the content summarization range may be analyzed by thecontent summarization unit. In the transformation operation 620, maintext for forming a summary sentence may be extracted based on theanalyzed information. In the synthesis operation 630, the summarizedcontent may be generated and output by arranging and editing theextracted text.

According to an example embodiment disclosed herein, the processor 130may perform content summarization by using the data summarization modelgenerated by using the learning algorithm described above in FIGS. 3,4A, and 4B, but this is only an example. The processor 130 may summarizethe content by other summarization methods. By way of example, theprocessor 130 may determine a main theme of the content summarizationunit through semantic analysis of the text included in the content,extract at least one representative sentence from each contentsummarization unit based on the determined main theme, and generate thesummarized content by arranging or synthesizing the representativesentence of each content summarization unit.

According to an example embodiment disclosed herein, the processor 130may perform generic summarization for summarizing content according to atype of content for general users and perform query-based summarizationfor summarizing content to be suitable for interests of a specific user.Further, the processor 130 may perform extractive summarization forextracting and summarizing sentences with meaning from the inputted textaccording to a type of summarized content and perform abstractivesummarization for modifying extracted sentences or generating andsummarizing new sentences.

Particularly, the processor 130 may generate different summarizedcontent for each content summarization unit. Based on the contentsummarization unit being a chapter or a paragraph, the processor 130 maygenerate summarized content 710 formed in a chapter unit or a paragraphunit as illustrated in FIG. 7A, and in response to the contentsummarization unit being an entire area, the processor 130 may generateone summarized content 720 regardless of the unit, such as, a chapter ora paragraph, as illustrated in FIG. 7B. In this case, the summarizedcontent 710, 720 may be displayed differently from currently displayedcontent 700. For example, the summarized content 710, 720 may bedisplayed to be brighter than the currently displayed content 700.

The processor 130 may generate the summarized content includingdifferent objects according to the type of the content. For example,when the type of content is news content or blog content, the processor130 may generate summarized content including text. When the type of thecontent is shopping content, the processor 130 may generate summarizedcontent including at least one of text and an image.

When content summarization is being performed, the processor 130 maycontrol the display 110 to display the summarized content in an areacorresponding to a location at which the user touch was input, from thedisplay screen where the content is displayed. For example, when userinput of touching the lower part of the display 110 is received while apart of the content 700 is displayed, the processor 130 may control thedisplay 110 to display the summarized content 710, 720 in the lower partof the display 110 as illustrated in FIG. 7A or FIG. 7B.

The processor 130 may provide detailed information through thesummarized content. To be specific, when user input of touching theupper part is received while a part of shopping content 800 isdisplayed, the processor 130 may summarize the information in an upperpart from among the areas which are not displayed in the display andcontrol the display 110 to display summarized content 810 along with apart of the content 800 as illustrated in FIG. 8A. In this case, thesummarized content 810 may include images of a product and textproviding information about the product (e.g., price information,detailed information, or store information). Further, as illustrated inFIG. 8A, when a user input of touching some image of the summarizedcontent 810 is received while a part of the shopping content 800 and thesummarized content 810 in the upper part are displayed, the processor130 may control the display 110 to display an enlarged image 820 in anarea where the summarized content 810 is displayed.

When user input is received on the summarized content, the processor 130may control the display 110 to display the area corresponding to a partat which the user input was received of the summarized content. To bespecific, as illustrated in FIG. 9A, when user input of touching a partof summarized content 900 (for example, step 2 website address) isreceived while a part of the content 900 and summarized content 910 aredisplayed, the processor 130 may control the display 110 to display anarea 920 where a part of the currently displayed content corresponds tothe location at which the user input was received as illustrated in FIG.9B. In this case, the processor 130 may generate new summarized content930 by changing the content summarization range of the summarizedcontent and controlling the display 110 to display the new summarizedcontent 930.

When user input for scrolling a screen is received while the summarizedcontent is displayed, the processor 130 may control the display 110 todisplay summarized content having a content summarization range changedwhile a screen of the content is scrolled. To be specific, asillustrated in FIG. 9A, when a user input for scrolling a page isreceived while a part of the content 900 and the summarized content 910are displayed, the processor 130 may control the display 110 to scrolland display a part of the currently displayed content as illustrated inFIG. 9B. In this case, the processor 130 may generate new summarizedcontent 930 by changing the content summarization range according to theuser input for scrolling the page and control the display 110 to displaythe new summarized content 930.

FIG. 10A is a flowchart provided to describe a method for summarizingcontent of an electronic apparatus according to an example embodimentdisclosed herein.

An electronic apparatus 100 displays content (S1010). In this case, theelectronic apparatus 100 may display only a part of the areas of thecontent.

Subsequently, the electronic apparatus 100 determines whether a userinput for content summarization is received (S1020). In this case, theuser input for content summarization may be a user touch of touching acertain area of a display.

The electronic apparatus 100 determines a content summarization rangeaccording to a location at which the user input was detected (S1030). Inthis case, the electronic apparatus 100 may determine as the contentsummarization range an area, corresponding to the user input, from amongthe content areas which are not displayed in the display.

The electronic apparatus 100 summarizes the content within the contentsummarization range according to the type of content (S1040). Theelectronic apparatus 100 may perform content summarization so thecontent is summarized by different content summarization units accordingto the type of the content.

The electronic apparatus 100 displays the summarized content along withthe content (S1050).

FIGS. 10B and 10C are diagrams provided to describe an example of usinga data summarization model according to an example embodiment disclosedherein.

In FIGS. 10B and 10C, a first component 2001 may be the electronicapparatus 100, and a second component 2002 may be a server 1100 (seeFIG. 11) where a data summarization model is stored. Further, the firstcomponent 2001 may include a universal general-purpose processor, andthe second component 2002 may include a dedicated processor for AI.Further, the first component 2001 may include at least one application,and the second component 2002 may include an Operating System (OS).

That is, the second component 2002 may be more integrated, morededicated (specialized), provide less delays, provide higherperformance, and/or provide a large amount of resources as compared withthe component 2001. Accordingly, the second component 2002 may becapable of processing a large number of calculations required togenerate, update, or apply the data summarization model more quickly andeffectively.

According to various example embodiments, a third component 2003 whichperforms similar functions as the second component 2002 may be added(see FIG. 10C).

In this case, an interface for transmitting/receiving data between thefirst component 2001 and the second component 2002 may be defined.

By way of example, an Application Program Interface (API) havingsummarized data for learning to be applied to the data summarizationmodel as a factor value may be defined. The API may be defined as a setof subroutines or functions called from any one protocol (for example, aprotocol defined in electronic apparatus 100) for any processingoperation of another protocol (for example, a protocol defined in theserver 1100). That is, an environment where any one protocol performs anoperation of another protocol may be provided through the API.

FIG. 10B is a flowchart provided to describe an example in which thefirst component summarizes content by using the data summarizationmodel.

The first component 2001 displays content (S2010). In this case, thefirst component 2001 may display only a part of the areas of thecontent.

The first component 2001 may determine whether a user input for contentsummarization is received (S2020). In this case, the user input forcontent summarization may be, without limitation, a user touch oftouching a certain area from the display.

The first component 2001 may determine the content summarization rangeaccording to the location at which the user input is detected (S2030).In this case, the first component 2001 may determine, as the contentsummarization range, an area corresponding to the user input from amongthe content areas which are not displayed on the display.

The first component 2001 may request summarization of the content withinthe determined content summarization range by the second component 2002(S2040).

The second component 2002 summarizes the content within the contentsummarization range according to the type of content (S2050). In thiscase, the second component 2002 may summarize the content so as to havedifferent content summarization units according to the type of content.

The second component 2002 may transmit the summarized content to thefirst component 2001 (S2060).

The first component 2001 may display the received summarized contentalong with the content (S2070).

FIG. 10C is a flowchart provided to describe an example in which thesecond component and the third component summarize the content by usingthe data summarization model according to a type of content,respectively.

According to an example embodiment, the first component 2001 and thesecond component 2002 may be components included in the electronicapparatus 100, and the third component 2003 may be a component locatedoutside the electronic apparatus 100, but the present disclosure is notlimited in this respect.

The first component 2001 displays content (S2110). In this case, thefirst component 2001 may display only a part of the areas of thecontent.

Subsequently, the first component 2001 determines whether a user inputfor content summarization is received (S2120). In this case, the userinput for content summarization may, for example, be a user touch oftouching a certain area of the display, but the present disclosure isnot limited in this respect.

The first component 2001 determines the content summarization rangeaccording to the location at which the user input is detected (S2130).In this case, the first component 2001 may determine, as the contentsummarization range, an area corresponding to the user input from amongthe content areas which are not displayed on the display.

The first component 2001 may request summarization of the content withinthe determined content summarization range by the second component 2002(S2140).

The second component 2002 may determine whether the type of content is atype which may be summarized in the electronic apparatus 100 (S2150).

As an example, when the content is text, the second component 2002 maysummarize the text by using a data summarization model configured tosummarize text included in the electronic apparatus 100 (S2160).Subsequently, the second component 2002 may transmit the summarizedcontent to the first component 2001 (S2170).

As another example, when the content is video, the second component 2002may request summarization of the content within the determined contentsummarization range by the third component 2003 (S2180). The thirdcomponent 2003 may summarize the video by using a data summarizationmodel configured to summarize video (S2190). In this case, thesummarized content may be a summarized video or a plurality of imagesgenerated by extracting a part(s) of frames of the video, for example.

The third component 2003 may transmit the summarized content to thefirst component 2001 (S2200).

The first component 2001 may display the received summarized contentalong with the content (S2210).

According to the above-described various example embodiments, the usercan be provided with summarized content for an area which is notcurrently displayed more intuitively and conveniently.

Meanwhile, in the above example embodiments, the electronic apparatus100 learns and summarizes data, but this is only an example, and theelectronic apparatus 100 may learn and summarize the data in conjunctionwith an external server.

FIG. 11 is a diagram provided to describe an example in which theelectronic apparatus 100 and the server 1100 learn and recognize datathrough interworking according to some example embodiments.

According to an example embodiment, the electronic apparatus 100 mayinterwork with the server 1100 over a local area network or by longdistance communication. The interworking of the electronic apparatus 100and the server 1100 may include an operation in which the electronicapparatus 100 and the server 1100 are connected directly or an operationin which the electronic apparatus 100 and the server 1100 are connectedthrough another component (for example, at least one of an Access Point(AP), a hub, a relay device, a base station, a router, and a gateway).

Referring to FIG. 3, the server 1100 may learn the criteria for contentsummarization, and the electronic apparatus 100 may perform contentsummarization by using a data summarization model generated based on alearning result of the server 1100.

In this case, the server 1100 may perform the functions of the datalearning unit 131 of FIG. 3. A model learning part 1140 of the server1100 may learn the criteria as to which data to use in order tosummarize the content and as to how to summarize the content by usingthe data. The model learning unit 1140 may learn the criteria forcontent summarization by acquiring summarized data to be used in thelearning operation and applying the acquired summarized data to the datasummarization model. A detailed description on the data summarizationmodel will be provided below.

By way of example, the model learning unit 1140 may allow the datasummarization mode to learn by using the content (for example, text, animage, and a video) and generate the data summarization model configuredto generate summarized information of the content. The generated datasummarization model may be a data summarization model configured tosummarize text, for example.

The summarization result providing part 132-4 of the electronicapparatus 100 may summarize the content by applying the summarized dataselected by the summarized data selection part 132-3 to the datasummarization model generated by the server 1100. By way of example, thesummarization result providing part 132-4 may transmit the summarizeddata selected by the summarized data selection part 132-3 to the server1100, and the server 110 may apply the data selected by the summarizeddata selection part 132-3 to the data summarization model. Accordingly,the summarization result providing part 132-4 may summarize the content.Further, the summarization result providing part 132-4 may receiveinformation on the summarized content generated by the server 1100 fromthe server 1100.

For example, when the content includes text, the server 1100 maysummarize the text by applying the text to the data summarization modelconfigured to summarize text and provide the summarized text to thesummarization result providing part 132-4 of the electronic apparatus100.

According to an example embodiment, the summarization result providingpart 132-4 of the electronic apparatus 100 may receive the datasummarization model generated by the server 1100 from the server 1100and summarize the content by using the received data summarizationmodel. In this case, the summarization result providing part 132-4 ofthe electronic apparatus 100 may summarize the content by applying thesummarized data selected by the summarized data selection part 132-3 tothe data summarization model received from the server 1100.

For example, when the content includes text, the server 1100 maysummarize the text by applying the text to a data summarization modelconfigured to summarize text received from the server 1100 and providethe summarized text to the processor of the electronic apparatus 100(for example, the processor 130 of FIG. 1). In this case, the processor130 may control the display (for example, the display 110 of FIG. 1) todisplay the summarized text.

The above-described methods may be realized as program instructionswhich are executable by diverse computer systems and recorded in anon-transitory computer readable medium. The non-transitory computerreadable medium may include program instructions, data files, and datastructures or combinations thereof. The program instructions recorded inthe medium may be specially designed and configured for the presentdisclosure or may have been publicly known and available to a personhaving ordinary skill in the computer software field. The non-transitorycomputer readable medium may include a hardware device which isspecially configured to store and execute program instructions, such as,magnetic mediums including a hard disk, a floppy disk, or a magnetictape, optical mediums including a Compact Disc Read-Only Memory (CD-ROM)or a Digital Versatile Disk (DVD), magneto-optical mediums including afloptical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM),and a flash memory. The program instructions may include a high-levellanguage code which is executable by a computer by using an interpreteras well as a machine language code generated by a compiler, for example.The hardware device may be configured to operate as one or more softwaremodules in order to perform the operations of the present disclosure,and vice versa.

Further, the above-described methods according to the exampleembodiments disclosed herein may be provided as computer programproducts.

The computer program products may include a software program, arecording medium which is readable by a computer with a softwareprogram, or a product transacted between a seller and a buyer.

By way of example, the computer program products may include a productin a form of a software program (for example, a downloadableapplication) which is electrically distributed through the electronicapparatus 100, a manufacturer of the electronic apparatus 100, or ane-market (for example, Google Play Store or App Store). For theelectrical distribution, at least some of the software program may bestored in a recording medium or may be temporarily generated. In thiscase, the recording medium may be a recording medium of a server of themanufacturer or the e-market or a recording medium or a relay server.

As noted above, the present disclosure is described by certain exampleembodiments and drawings, but the disclosure is not limited to theseexamples. The present disclosure may be modified and changed by a personhaving ordinary skill in the art. The scope of the present disclosureshould not be limited by the example embodiments disclosed herein andshould defined by the claims provided below and the equivalents thereof.

What is claimed is:
 1. A method for summarizing content of an electronicdevice, the method comprising: displaying a first portion of the contenton a display; receiving a first touch input, while the first portion ofthe content is being displayed; determining a position where the firsttouch input is received on the display; identifying a second portion ofthe content different from the first portion of the content, based onthe determined position on the display, wherein the second portion ofthe content is identified from among a plurality of differentnon-displayed portions of the content; obtaining summarizationinformation related to the second portion of the content, by an AI(Artificial Intelligence) model; displaying, on an area corresponding tothe determined position on the display, the summarization informationalong with the first portion of the content on the display; receiving asecond touch input on a portion of the summarization information whilethe summarization information is displayed on the display along with thefirst portion of the content; and in response to the second touch input,displaying at least a part of the second portion of the content on thedisplay, wherein an amount of the at least a part of the second portionof the content is greater than an amount of the summarizationinformation.
 2. The method as claimed in claim 1, further comprising:determining a type of the content by analyzing the content; andobtaining the summarization information related to the second portion ofthe content based on the determined type of the content.
 3. The methodas claimed in claim 1, further comprising: identifying, from among aplurality of different content summarization units, a contentsummarization unit based on a type of the first touch input, wherein thesummarization information is obtained based on the identified contentsummarization unit.
 4. The method as claimed in claim 1, furthercomprising: scrolling the displayed content, based on receiving inputfor scrolling while the summarization information is displayed, thesummarization information having a content summarization range changedaccording to the screen scrolling.
 5. The method as claimed in claim 1,wherein the summarization information is obtained based on a datasummarization model generated using a learning algorithm.
 6. The methodas claimed in claim 1, wherein the obtaining of the summarizationinformation comprises: analyzing information of the second portion ofthe content; extracting main text for a summary sentence based on theanalyzed information; and obtaining the summarization information byediting and arranging the extracted text.
 7. The method as claimed inclaim 1, wherein, based on the second portion of the content comprisingshopping content for a plurality of products, the summarizationinformation comprises images of the products and text about theproducts.
 8. The method as claimed in claim 1, wherein the first touchinput comprises a touch input to any of upper, lower, right, and leftareas of the display.
 9. An electronic device comprising: a display; anda processor configured to: display a first portion of the content on adisplay; receive a first touch input, while the first portion of thecontent is being displayed; determine a position where the first touchinput is received on the display; identify a second portion of thecontent different from the first portion of the content, based on thedetermined position on the display, wherein the second portion of thecontent is identified from among a plurality of different non-displayedportions of the content; obtain summarization information related to thesecond portion of the content, by an AI (Artificial Intelligence) model;display, on an area corresponding to the determined position on thedisplay, the summarization information along with the first portion ofthe content on the display; receive a second touch input on a portion ofthe summarization information while the summarization information isdisplayed on the display along with the first portion of the content;and in response to the second touch input, display at least a part ofthe second portion of the content on the display, wherein an amount ofthe at least a part of the second portion of the content is greater thanan amount of the summarization information.
 10. The device as claimed inclaim 9, wherein the processor is further configured to: determine atype of the content by analyzing the content; and obtaining thesummarization information based on the determined type of the content.11. The device as claimed in claim 9, wherein the processor is furtherconfigured to, identify, from among a plurality of different contentsummarization units, a content summarization unit based on a type of thefirst touch input, wherein the summarization information is obtainedbased on the identified content summarization unit.
 12. The device asclaimed in claim 9, wherein the processor is further configured to,scroll the displayed content, based on receiving input for scrollingwhile the summarization information is displayed, the summarizationinformation having a content summarization range changed according tothe screen scrolling.
 13. The device as claimed in claim 9, wherein thesummarization information is obtained based on a data summarizationmodel generated using a learning algorithm.
 14. The device as claimed inclaim 9, wherein the processor is further configured to: analyzeinformation of the second portion of the content; extract main text fora summary sentence based on the analyzed information; and obtain thesummarization information by editing and arranging the extracted text.15. The device as claimed in claim 9, wherein, based on the secondportion of the content comprising shopping content for a plurality ofproducts, the summarization information comprises images of the productsand text about the products.
 16. The device as claimed in claim 9,wherein the first touch input comprises a touch input to any of upper,lower, right, and left areas of the display.
 17. A non-transitorycomputer readable recording medium including a program for executing acontrolling method of an electronic device comprising: displaying afirst portion of the content on a display; receiving a first touchinput, while the first portion of the content is being displayed;determining a position where the first touch input is received on thedisplay; identifying a second portion of the content different from thefirst portion of the content, based on the determined position on thedisplay, wherein the second portion of the content is identified fromamong a plurality of different non-displayed portions of the content;obtaining summarization information related to the second portion of thecontent, by an AI (Artificial Intelligence) model; displaying, on anarea corresponding to the determined position on the display, thesummarization information along with the first portion of the content onthe display; receiving a second touch input on a portion of thesummarization information while the summarization information isdisplayed on the display along with the first portion of the content;and in response to the second touch input, displaying at least a part ofthe second portion of the content on the display, wherein an amount ofthe at least a part of the second portion of the content is greater thanan amount of the summarization information.